This paper presents an effective biogeography-based optimization (BBO) for optimal location and sizing of solar photovoltaic distributed generation (PVDG) units to reduce power losses while maintaining voltage profile and voltage harmonic distortion at the limits. This applied algorithm was motivated by biogeography, that the study of the distribution of biological species through time and space. This technique is able to expand the searching space and retain good solution group at each generation. Therefore, the applied method can significantly improve performance. The effectiveness of the applied algorithm is validated by testing it on IEEE 33-bus and IEEE 69-bus radial distribution systems. The obtained results are compared with the genetic algorithm (GA), the particle swarm optimization algorithm (PSO) and the artificial bee colony algorithm (ABC). As a result, the applied algorithm offers better solution quality and accuracy with faster convergence.

In this paper, a novel improved Stochastic Fractal Search optimization algorithm (ISFSOA) is proposed for finding effective solutions of a complex optimal reactive power dispatch (ORPD) problem with consideration of all constraints in transmission power network. Three different objectives consisting of total power loss (TPL), total voltage deviation (TVD) and voltage stabilization enhancement index are independently optimized by running the proposed ISFSOA and standard Stochastic Fractal Search optimization algorithm (SFSOA). The potential search of the proposed ISFSOA can be highly improved since diffusion process of SFSOA is modified. Compared to SFSOA, the proposed method can explore large search zones and exploit local search zones effectively based on the comparison of solution quality. One standard IEEE 30-bus system with three study cases is employed for testing the proposed method and compared to other so far applied methods. For each study case, the proposed method together with SFSOA are run fifty run and three main results consisting of the best, mean and standard deviation fitness function are compared. The indication is that the proposed method can find more promising solutions for the three cases and its search ability is always more stable than those of SFSOA. The comparison with other methods also give the same evaluation that the proposed method can be superior to almost all compared methods. As a result, it can conclude that the proposed modification is really appropriate for SFSOA in dealing with ORPD problem and the method can be used for other engineering optimization problems.

This paper applies a proposed modified stochastic fractal search algorithm (MSFS) for dealing with all constraints of optimal reactive power dispatch (ORPD) and finding optimal solutions for three different cases including power loss optimization, voltage deviation optimization, and L-index optimization. The proposed MSFS method is newly constructed in the paper by modifying three new solution update mechanisms on standard stochastic fractal search algorithm (SSFS). The first modification is to keep only one formula and abandon one formula in the diffusion process while the second modification and the third modification are used in the first update and the second update. In two updates of SSFS, solutions with low quality are updated with high probability while other solutions with high quality do not get chances to be updated. This manner results in the fact that some promising solutions around the high quality solutions can be missed. In order to tackle this restriction, the second modification of MSFS is to newly update the worst solutions in the first update and the best solutions in the second update. In the third modification, all existing formulas of SSFS in the two updates are abandoned and the same new proposed technique is used for updating such solutions in two updates. Compared to SSFS, the three modifications can bring advantages to MSFS such as using smaller number of produced solutions per iteration, spending shorter execution time, finding better optimal solutions, and owning more stable search ability. Furthermore, the proposed method also sees its effectiveness and robustness over SSFS by testing on IEEE 30-bus system and IEEE 118-bus system with three different single objectives for each system. The proposed method can find less minimum, average, and maximum for all the cases in addition to faster search speed. Besides, the proposed method is also compared to other methods such as PSO-based method group, GA-based method group, DE-based method group, and other recent methods. Result comparisons also indicate that the proposed method can be more efficient than almost all these methods with respect to less minimum and smaller values of control parameters. As a result, evaluation of the performance of the proposed method is that it should be used for seeking solutions of ORPD problem.

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This paper proposes an Adaptive Selective Cuckoo Search Algorithm (ASCSA) for solving bi-objective short-term hydrothermal scheduling (BOSTHTS). The major purpose of the BOSTHTS problem is to reduce electric generation fuel cost and polluted emission from all considered thermal generating units over a scheduled intervals. In addition, the problem also takes all constraints of hydrothermal power systems such as real power demand of load, limitations of generations and limitations of reservoirs into account. The proposed method is constructed by tackling all drawbacks of classical Cuckoo Search algorithm (CCSA) so as to shorten number of iterations and fast converge to good solutions. The proposed method together with CCSA and another modified version of CCSA (MCSA) are implemented for two different systems with different types of fuel cost form and emission form and results from these methods are also compared to other existing methods. Objective comparisons and computation time comparisons indicate that the proposed method is superior to CCSA, MCSA and other compared methods. As a result, the proposed method is considered to be a strong optimization method for the considered BOSTHTS problem.

In this article, a method is proposed to increase the accuracy of reactive power‐sharing for parallel‐connected inverters in Microgrid, this method is done by automatically adjusting the values of the virtual impedance to adjusting the output voltage of the inverters. The virtual impedances are automatically adjusted to compensate for the difference in the output voltage of the inverters due to the influence of the line impedances. The output voltage of the inverters will be adaptively adjusted according to the change of the load, this method greatly improves the accuracy in the reactive power sharing The correct power sharing for the inverters will ensure the stability of voltage and frequency in the Microgrid. The control method is simple and does not need to know the line impedance parameter. The feasibility and effectiveness of the proposed strategy are proven by simulation and experimental results.